To train and evaluate the proposed \(\text{TC-HGR}\)  architecture, we used the second \(\text{Ninapro}\)  dataset [1]} called DB2, which is a widely used public dataset. More specifically, DB2 is collected by the Delsys Trigno Wireless EMG system, which has 12 channels and records the electrical activities of muscles at 2 kHz. Moreover, the DB2 dataset consists of the sEMG signals from 40 healthy subjects (28 males and 12 females with age \(29.9 \pm 3.9\)  years among which 34 are right-handed and six are left-handed) performing 50 different hand gestures. Each gesture is repeated 6 times, each lasting for 5 seconds followed by 3 seconds of rest. The 50 different gestures in the DB2 dataset are presented in three sets of exercises (i.e., B, C, and D). In this paper, we focus on Exercise B, which consists of 17 different gestures. More specifically, Exercise B consists of 9 basic wrist movements with 8 isometric and isotonic hand movements.
Following the recommendations provided by the \(\text{Ninapro}\)  dataset, the training set consists of \(2/3\)  of the repetitions of each gesture (i.e., \(1, 3, 4\) , and 6), and the test set consists of the remaining repetitions (i.e., 2 and 5). Please refer to [1]} for a more detailed description of the \(\text{Ninapro}\)  database.
